110023 research outputs found
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Astragalus membranaceus Extract Prevents Calcium Oxalate Crystallization and Extends Lifespan in a Drosophila Urolithiasis Model
No full text[[abstract]]Approximately 1 in 20 people develops kidney stones at some point in their life. Although the surgical removal of stones is common, the recurrence rate remains high and it is therefore important to prevent the occurrence of kidney stones. We chose Astragalus membranaceus (AM), which is a traditional Chinese medicine, to study the prevention of urolithiasis using a Drosophila model based on our previous screening of traditional Chinese herbs. Wild-type Drosophila melanogaster Canton-S adult fruit flies were used in this study. Ethylene glycol (EG, 0.5%) was added to food as a lithogenic agent. The positive control agent (2% potassium citrate (K-citrate)) was then compared with AM (2, 8, and 16 mg/mL). After 21 days, the fruit flies were sacrificed under carbon dioxide narcotization, and the Malpighian tubules were dissected, removed, and processed for polarized light microscopy examination to observe calcium oxalate (CaOx) crystallization. Then, the ex vivo dissolution of crystals in the Malpighian tubules was compared between K-citrate and AM. Survival analysis of the EG, K-citrate, and AM groups was also performed. Both 2% K-citrate and AM (16 mg/mL) significantly inhibited EG-induced CaOx crystal formation. Mean lifespan was significantly reduced by the administration of EG, and the results were significantly reversed in the AM (8 and 16 mg/mL) groups. However, AM extract did not directly dissolve CaOx crystals in Drosophila Malpighian tubules ex vivo. In conclusion, AM extract decreased the ratio of CaOx crystallization in the Malpighian tubules and significantly ameliorated EG-induced reduction of lifespan. AM prevented CaOx crystal formation in the Drosophila model
Neurological, Psychiatric, and Psychological Implications of the COVID-19 Pandemic: Protocol for a Large-Scale Umbrella Review of Observational Studies
No full text[[abstract]]The severe acute respiratory syndrome coronavirus 2 disease (SARS-CoV-2) is the most severe manifestation of the coronavirus disease 2019 (COVID-19) pandemic. Accruing evidence indicates that the COVID-19 pandemic may have profound deleterious neurological, psychiatric, and psychological outcomes. The number of systematic reviews (SRs) and meta-analyses (MAs) on this topic has grown exponentially. This protocol aims to synthesize all evidence from SRs and MAs on the associations between the COVID-19 pandemic and neuropsychiatric outcomes. The following electronic databases will be systematically searched from inception up to 15 January 2022: PubMed, Embase, APA PsycINFO, and Cochrane Reviews. An umbrella review (UR) of SRs and MAs of observational studies will be conducted. SRs and/or MAs of observational studies examining any direct or indirect association of COVID-19 with the neuropsychiatric outcomes will be deemed eligible for potential inclusion in this UR. The direct associations include the impact on the (1) prognosis of COVID-19 and (2) neuropsychiatric sequelae after COVID-19 infection. The indirect associations include the influence of the COVID-19 pandemic on the (1) treatments and (2) outcomes of neurological and psychiatric conditions associated with the COVID-19 pandemic
The Association between Parenting Stress, Positive Reappraisal Coping, and Quality of Life in Parents with Autism Spectrum Disorder (ASD) Children: A Systematic Review
No full text[[abstract]]Parents with autism spectrum disorder (ASD) children generally suffer from poor coping and an impaired quality of life (QoL). This systematic review investigates parenting stress, positive reappraisal coping, and QoL in parents with ASD children. A literature search was carried out for publications written in English on the selected investigation topics using five databases, namely, Scopus, PubMed, Wiley, ScienceDirect Online, and EBSCO. Only studies investigating or measuring parenting stress, positive reappraisal coping, and QoL in ASD were included. Our results indicate that parents with ASD children have high stress levels. This is associated with the ineffective use of coping strategies and a low QoL. Adaptive coping strategies are related to a higher QoL, whereas maladaptive coping techniques are related to a worse QoL. Positive reappraisal coping is negatively correlated to meaningfulness, and it is used by parents to change their daily routines in order to motivate themselves towards new and evolving goals in life. Finally, we found a significant negative correlation between parenting stress, positive reappraisal coping, and the QoL of parents with ASD children. Positive reappraisal coping as a strategy helps parents adapt to stress and was found to be a potential mediatory function between parental stress and QoL
國、高中生之網路遊戲可近性、同儕影響、網路遊戲正向預期、幸福感及網路遊戲成癮之路徑探討
No full text[[abstract]]Purpose: Previous studies indicated that accessibility and peer influence predicted internet gaming addiction among adolescents, but few studies have investigated the underlying process. Based on the literature review, we hypothesized that positive internet gaming use expectancy would mediate the links between accessibility, peer influence and internet gaming addiction, and that well-being would moderate this mediating effect. Methods: This study is part of an investigation of teen internet use conducted in 2015 in which participants were recruited from junior and senior high schools throughout Taiwan using both stratified and random cluster sampling. The valid samples were composed of 2,806 junior high school and 3,312 senior high school students. After obtaining the students' and their parents' informed consent, the students completed questionnaires in class on background characteristics, internet gaming accessibility, peer influence, positive internet gaming use expectancy, well-being, and internet gaming addiction. Results: The results in both junior and senior high school samples consistently indicated that peer influence predicted internet gaming addiction, and that this relationship was fully mediated by positive internet gaming use expectancy. Accessibility directly and indirectly predicted internet gaming addiction, mediated by positive internet gaming use expectancy. However, the level of well-being significantly moderated the mediating effect of positive internet gaming use expectancy in linking internet game accessibility and peer influence to internet gaming addiction. Further analyses indicated that the predictive power of positive internet gaming use expectancy on internet gaming addiction weakened as the level of well-being increased. Conclusions: These findings provide empirical evidence to support the mediating role of internet gaming expectancy and the protective role of well-being in the link between social risk factors and internet gaming addiction among adolescents. Our findings clarify the mechanism through which peer influence has its impact on gaming addiction and can be used to design prevention and treatment strategies for gaming addiction among adolescents
A bagging ensemble machine learning framework to predict overall cognitive function of schizophrenia patients with cognitive domains and tests
No full text[[abstract]]Background
It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.
Methods
To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.
Results
The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.
Conclusion
The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests
Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals
No full text[[abstract]]Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application
Active Authentication Protocol for IoV Environment with Distributed Servers
No full text[[abstract]]The Internet of Vehicles (IoV) has evolved as an advancement over
the conventional Vehicular Ad-hoc Networks (VANETs) in pursuing a more
optimal intelligent transportation system that can provide various intelligent
solutions and enable a variety of applications for vehicular traffic. Massive
volumes of data are produced and communicated wirelessly among the different relayed entities in these vehicular networks, which might entice adversaries
and endanger the system with a wide range of security attacks. To ensure the
security of such a sensitive network, we proposed a distributed authentication
mechanism for IoV based on blockchain technology as a distributed ledger
with an ouroboros algorithm. Using timestamp and challenge-response mechanisms, the proposed authentication model can withstand several security
attacks such as Man-in-Middle (MiM) attacks, Distributed Denial of Service
(DDoS) attacks, server spoofing attacks and more. The proposed method also
provides a solution for single-point failure, forward secrecy, revocability, etc.
We exhibit the security of our proposed model by using formal (mathematical)
analysis and informal analysis. We used Random Oracle Model to perform
the mathematical analysis. In addition, we compared the communication cost,
computation cost, and security of the proposed model with the related existing
studies. We have verified the security of the model by using AVISPA tool
simulation. The security analysis and computation analysis show that the
proposed protocol is viable
Design and Implementation of an ML and IoT based Adaptive Traffic Management System for Smart Cities
No full text[[abstract]]The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience
Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals
No full text[[abstract]]Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals
Benefits of applying virtual reality in pelvic movement training through a Wii Fit: a randomized controlled trial
No full text[[abstract]]Background: Pelvic movement training has become compulsory for part of medical students. An increasing amount of research has focused on the influence of virtual reality (VR) on learning effectiveness. However, its application to pelvic floor muscles or pelvic movement training is still in its infancy. We compared the effectiveness of conventional pelvic movement training with or without VR-assisted pelvic movement training for student learning.
Methods: We recruited 44 university students (16 male and 28 female participants; average age = 19.7 ± 0.31 years) who had not previously received pelvic movement education or training. The participants were randomly assigned into traditional and experimental groups to acquire pelvic movements and relevant knowledge. The traditional group received conventional classes (about 15 min), whereas the experimental group received both conventional classes and VR-assisted teaching (additional VR session took approximately 25-45 min depending on the speed of movement of each participant). The participants were asked to control the trajectory of the centre of pressure on the Wii Fit balance board and build-in games to learn pelvic movements. We conducted evaluations before, immediately after, and 2 weeks after the experiment, based on the scores of written and practical examinations. The experimental group was also asked to complete a questionnaire during the posttest.
Results: We carried out two-way repeated measures ANOVA and discovered that the written examination scores indicated a significant Time × Group interaction (p=0.015). In each group, the written and practical examinations in the posttest and follow-up test exhibited significantly improved results compared with the baseline value (p <0.001, except for traditional group of written exam in follow up test vs. baseline p=0.001). The written examination in the follow-up test did not decline significantly compared with those in the posttest, but the practical examination in the follow-up test was decline significantly compared with those in the posttest (p=0.033). The experimental group had superior overall performance in the practical examinations than the traditional group (experimental group: mean = 76.27, 95% confidence level [CI] = 70.84-81.71; traditional group: mean = 64.21, 95% CI = 58.78-69.65). No significant difference in the written examination between two groups. The percentage for agreement ratio on the usefulness, ease of use, users' intention to continue using the VR-assisted teaching is high (95.5-100%).
Conclusions: The results of this study suggested that conventional and conventional + VR teaching were both effective. However, the incorporation of VR stimulated learning motivation and facilitated precise performance of pelvic movements. It is recommended that pelvic floor muscles training could be supplemented with VR or games to increase students' motivation and understanding how to perform pelvic movements